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Dear R-users, I was able to make ROracle package to connect to the DB (Oracle91, 64bit, on Solaris). But after executing siple SQL query, "fetch" commaned gives me an empty dataframe. ### RORACLE INSTALATION PROCEDURE ### R CMD INSTALL --configure-args='--enable-extralibs' --enable-oracle32=no ~/tmp/ROracle_0.5-5.tar.gz #since we have 64bit Oracle9i instalation PROBLEM:

Dear R-users, In order to use ROracle (v. 0.5-5) package I compiled R-1.9.1 on Linux (2.4.20-28.9smp) and installed it. I need to use Oracle9i on Solaris. When executing "library(ROracle)" I got the following error message: > library(ROracle) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

Dear R users, I'm coming back to R after while. I have a data frame with 200 columns, each column has a name. How to extract all columns to a new dataset, but the specified (by names) ones? I was playing with that for a little bit using the vector syntax but got several syntax errors. Thanks, Rado

Dear R users, I'm working on implementation of hydrometeorological DB in Oracle9i and would like to use R, as a plotting engine for graphs and maps and also to perform some non trivial statistics on DB data using R. Thus any links with examples or efforts on: - general DB and R cooperation - Oracle91 and R cooperation - Oracle PL/SQL and R are welcomed. Thanks in advance, Rado --

Dear R-users Sorry for posting the previous message too soon before I have finished it. I have matrix (mprecip) with headers: > dim(mprecip) [1] 6268 170 > mprecip date GilzeRijen Eindhoven Volkel ZuidLimburg Arcen Ubachsberg 1 01/01/1978 NA 0.0 NA 0.1 NA NA 2 01/02/1978 NA 0.0 NA 0.0 NA NA 3

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

Hello R-users, Is there any more sophisticated way how to identify the dataset outliers other then seeing them in boxplot? I wanna exclude them from further analysis and I am interested in their position in my vector data. Rado -- Radoslav Bonk M.S. Dept. of Physical Geography and Geoecology Faculty of Sciences, Comenius University Mlynska Dolina 842 15, Bratislava, SLOVAKIA tel: +421 2 602

I don't get the same result, do you have a package loaded that would change the default behavior (such as Hmisc)? > list(grp.1) [[1]] [1] 1 2 Levels: 1 2 > list(grp.1[mask]) [[1]] [1] 1 Levels: 1 2 > library(Hmisc) <<snip>> > list(grp.1[mask]) [[1]] [1] 1 Levels: 1 --Matt > version _ platform i386-pc-mingw32 arch i386 os

Hi R-users, How can I found the number of a distinct values in a data frame (occurrence of distinct values)? The dataframe consists of several thousands integer numbers. Thanks, Rado -- Radoslav Bonk M.S. Dept. of Physical Geography and Geoecology Faculty of Sciences, Comenius University Mlynska Dolina 842 15, Bratislava, SLOVAKIA tel: +421 905 968 127 e-mail: rbonk at host.sk

Hi all, I'm using R 1.5.1 on Windows 2000. The following snippet of code doesn't seem to do anything - no error is reported, and there is no name change. names(myFrame[,c(1:3)]) <- c("name1", "name2", "name3") This code however works nicely: names(myFrame)[c(1:3)] <- c("name1", "name2", "name3") Can anyone suggest why

Greetings all, I'm trying to figure out how to calculate the inverse CDF (i.e. a quantile) for a non-central F distribution. I could put together a quick numerical solver routine using the CDF, but I wonder if there's a function that I've missed that would be more efficient? Thank-you, Andrew Andrew Robinson Ph: 208 885 7115 Department of Forest Resources Fa: 208 885

Hello, I have been searching for ways to obtain these for combinations of fixed factors and levels other than the 'baseline' group (contrasts coded all 0's) from a mixed-effects model in lme. I've modelled the continuous variable y as a function of a continuous covariate x, and fixed factors A, B, and C. The fixed factors have two levels each and I'd like to know whether

Dear R-users, I have two outliers related questions. I. I have a vector consisting of 69 values. mean = 0.00086 SD = 0.02152 The shape of EDA graphics (boxplots, density plots) is heavily distorted due to outliers. How to define the interval for outliers exception? Is <2SD - mean + 2SD> interval a correct approach? Or should I define 95% (or 99%) limit of agreement for data interval,

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

Hi R-users, I have a set of elevation residuals as geodata points. I would like to display them in the following way: -negative values using pch=20 (filled circle) -positive values using pch=1 (empty circle) while using the cex.max=5, cex.min=0.1 for points() to represent the residuals value. Basically I would like to distinguish neagtive and positive values at my map. How to do this. Thanks

I have two lists: xa <- list( X=c(1,2,3), Y=c(4,5,6), Z=c(7,8,9) ) xb <- with( barley, tapply( X=seq(1:nrow(barley)), INDEX=site , FUN=function(z)yield[z])) I can convert xa to a dataframe easily with: as.data.frame(xa) But if i try the same with xb I get: as.data.frame(xb) Error in as.data.frame.default(xb) : can't coerce array into a data.frame What

Why does stop("we are done") print "Error in eval.with.vis(expr, envir, enclos) :" ? It would seem to me that a plain stop() is not an error, and that it would make more sense to have an error() function that is different from a stop(). Is there a rationale here that I am missing? sincerely, /iaw

Hi, Does anybody know suitable documents (manuals) on writing user functions (covering loops, conditions ...) in R? Other than the usually available manuals. Thanks, Rado -- Radoslav Bonk M.S. Dept. of Physical Geography and Geoecology Faculty of Sciences, Comenius University Mlynska Dolina 842 15, Bratislava, SLOVAKIA tel: +421 905 968 127 e-mail: rbonk at host.sk

Hi R-users, My question is more methodological one, rather than technical. I have to samples representing residuals based on two measurements techniques (resid1,resid2; n=69). I need to compare two samples, to reject one technique (the worse one), and to keep the one which gave lower residuals (better one). What to look for? What should I analyse? Means, variance, std. deviations? Based on

Hi R-users, I am using "postscript("xx.eps", horizontal=FALSE, onefile=FALSE, height=6, width=10, pointsize=10)" and "barplot(xx.dat)" in R to produce EPS graphics for LATeX document. When checking the graphics with "gv" I see that it was not produced with full details. The main graphic is there, but axes labels and titles are missing. While checking the